Genomic landscape of lung adenocarcinoma in East Asians

Abstract

Lung cancer is the world’s leading cause of cancer death and shows strong ancestry disparities. By sequencing and assembling a large genomic and transcriptomic dataset of lung adenocarcinoma (LUAD) in individuals of East Asian ancestry (EAS; n = 305), we found that East Asian LUADs had more stable genomes characterized by fewer mutations and fewer copy number alterations than LUADs from individuals of European ancestry. This difference is much stronger in smokers as compared to nonsmokers. Transcriptomic clustering identified a new EAS-specific LUAD subgroup with a less complex genomic profile and upregulated immune-related genes, allowing the possibility of immunotherapy-based approaches. Integrative analysis across clinical and molecular features showed the importance of molecular phenotypes in patient prognostic stratification. EAS LUADs had better prediction accuracy than those of European ancestry, potentially due to their less complex genomic architecture. This study elucidated a comprehensive genomic landscape of EAS LUADs and highlighted important ancestry differences between the two cohorts.

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Fig. 1: Driver genes for EAS LUADs.
Fig. 2: CNVs and mutation signature analysis.
Fig. 3: Transcriptomic clusters in EAS and EUR cohorts.
Fig. 4: Ancestry differences in therapeutic opportunities.
Fig. 5: Survival groups and cohort differences.

Data availability

Raw sequencing data have been deposited in the European Genome-phenome Archive (EGA, http://www.ebi.ac.uk/ega/) under accession codes EGAD00001004421 and EGAD00001004422. All clinical records, somatic mutations, copy number variations and histological images from our study are hosted in OncoSG (https://src.gisapps.org/OncoSG/) under dataset ‘Lung Adenocarcinoma (GIS, 2019)’ which is publicly available (Supplementary Note).

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Acknowledgements

This work was funded by Glaxo Wellcome Manufacturing Pte Ltd, the Agency for Science Technology and Research (A*STAR) (grant no. GIS/15-IAF100), the National Medical Research Council, Singapore (grant nos. NMRC/OFLCG/002c/2018, NMRC/OFIRG/0064/2017 and NMRC/TCR/007-NCC/2013), the National Research Foundation, Singapore (grant no. NRF-NRFF2015–04), German Cancer Aid (grant no. 70113510) and Lung Cancer Consortium Singapore (LCCS). LCCS is jointly supported by philanthropy (including Singapore Millennium Foundation), and institutional and industrial grants. W.Z. is supported in part by the National Key R&D program of China (grant nos. 2018YFC0910400 and 2018YFC1406902) and the National Science Foundation of China (grant no. 31970566). We thank Beijing Genomics Institute for providing the published sequencing data. We thank T. Zhang for contribution to genomic data analysis, C. T. J. Ong, Y. L. Lee, I. M. L. Chua and W. W. J. Soon for the next-generation sequencing work, the GIS Research Pipeline Development team for support with analysis pipelines and Y. Matsuoka for administrative support. We thank Y. Cun for helpful discussions.

Author information

A.M.H., W.Z. and D.S.W.T. conceived the study, and B. Lim, W.L.T. and E.-H.T. contributed. A.M.H. coordinated the genomics work. D.S.W.T. coordinated the clinical work. J.C. and W.Z. coordinated work on data analysis. J.C. performed genomic data analysis with contributions from H.Y., C.Q.T., B. Lu, J.J.S.A., J.Q.L., F.G.S., R.N., Y.Y.L., C.Z.J.P., K.P.C., Y.F.L. and J.L. A.W. contributed to analysis pipeline development. A.S.M.T. and L.B.A. performed nucleic acid extraction, exome library preparations and fusion gene validation, with contributions from F.G.S. who also performed SNV validation. A.T., with assistance from Z.W.A. and T.K.H.L., performed sectoring and histology studies and led the pathological work. P.S.C. and P.Y.N. contributed to RNA-seq library preparations and sequencing. T.P.T.K., B.-H.O., D.A., A.A.L.H., A.G. and C.W.T. performed surgery and biopsy procedures. D.S.W.T., A.T., W.-T.L., C.K.T., L.W. and E.-H.T. coordinated patient tissue banking, specimen transfer and clinical data curation. P.J.C., M.M.C., J.J.S.A. and A.J.S. implemented the OncoSG data portal. L.S., Z.W.A. and J.P.S.Y. performed multiplex immunohistochemistry. J.C., W.Z., A.M.H., D.S.W.T., C.L.C. and E.-H.T. interpreted the data and conceptualized the manuscript. J.C. created figures with contributions from F.G.S., C.Q.T., J.J.S.A., S.M., K.P.C. and W.Z. J.C. and W.Z. wrote the manuscript, with contributions from D.S.W.T., A.M.H., C.L.C., S.M., A.S.M.T., J.J.S.A., H.Y., B. Lu, K.P.C. and E.-H.T.

Correspondence to Daniel Shao Weng Tan or Axel M. Hillmer or Weiwei Zhai.

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A.M.H, D.S.W.T., and W.Z. received research funding from Glaxo Wellcome Manufacturing Pte Ltd.

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Extended data

Extended Data Fig. 1 Kaplan-Meier plots of driver genes that can stratify patient survival outcome.

Survival outcomes of patients harbouring the driver mutation were compared against those did not in the (a) East Asian- ancestry (EAS, n=293) and (b) European-ancestry (EUR, n=225) cohort. Genes tested are from a curated list of LUAD drivers (Methods), and the plotted genes are those show significant coefficients (FDR<0.2 in EAS and p-values<0.05 in EUR, two-sided t-test) in a multivariate Cox model including stage, age, gender and smoker in any cohort. (c) Comparision of the survival outcomes of mutant or wildtype EGFR carriers among the EAS non-smokers (n=185) and smokers (n=110). EGFR mutant carriers showed better outcome, especially among non-smokers. Mut, mutant; WT, wildtype.

Extended Data Fig. 2 PCA of RNA profiles using both tumor and normal samples.

PCA of (a) East Asian-ancestry (EAS) and (b) European-ancestry (EUR) tumor (EAS, n=172; EUR, n=249) and normal (EAS, n=88; EUR, n=42) samples to illustrate the relationship of LUAD transcriptomic subtypes and the normal samples. In the two-group partitions, the TRU clusters were closer to the normal samples in both cohorts. In the three-group partitions, the TRU and TRU-I sub-clusters in the Asian were closer to the normal samples, while the TRU sub-cluster in the EUR was closer to the normal samples.

Extended Data Fig. 3 Phenotypes of the RNA sub-clusters in the EUR cohort.

The top two rows indicate the cluster assignment of the patients. The following rows show the normalized mean expression of GSEA enriched gene sets from the differential expressed genes between the TRU and non-TRU clusters and between the PI and PP sub-clusters, and the values of immune-related signatures. High values were shown in red and low in blue. The oncoprint plot shows major driver mutations across sub-clusters. The clinical and other genomic phenotypes are shown at the bottom.

Extended Data Fig. 4 Kaplan-Meier plots of the survival groups derived from genomic features only.

Using only genomic features (driver genes, molecular and ITH features), patients were divided evenly into three survival groups based on the predicted hazard from the multivariate Cox model. For both East Asian-ancestry (a) and European-ancestry (b) cohort, these survival models can clearly stratify patient survival outcome. They could stratify survival outcome even within early or late stage patients, indicating the prediction power of genomic features independent of clinical features. Statistical test used can be found under Methods section “Statistics and Reproducibility”.

Extended Data Fig. 5 Comparison of the prediction accuracies between the balanced EAS and EUR cohorts.

Related to Fig. 5c, box plots showing prediction accuracy calculated as Harrell’s concordance index (c-index) from the multivariate Cox models with different set of predictors. For fair comparisons across cohorts, the proportion of smokers were balanced by randomly down-sampling non-smokers in the East Asian-ancestry cohort and smokers in the European-ancestry cohort (a). To rule out the effect of EGFR mutation and possible TKI treatment on patient survival, the comparison was narrowed down to only patients with wildtyp EGFR (b). Statistical test used and the definition of boxplot elements can be found under Methods section “Statistics and Reproducibility”.

Extended Data Fig. 6 Summary of the ancestry differences.

A summary of major ancestry differences across the two cohorts in this study (top), and the differences seen when comparing among smokers and non-smokers (bottom). Red, higher/more; blue, lower/less; ≈, similar; ♂, male; ♀, female; IO, immuno-oncology; NA, not available.

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Chen, J., Yang, H., Teo, A.S.M. et al. Genomic landscape of lung adenocarcinoma in East Asians. Nat Genet 52, 177–186 (2020). https://doi.org/10.1038/s41588-019-0569-6

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